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The architecture of a modular behavioral agent (MBA) with learning ability for hardware realization is proposed to implement a multilevel behavioral robot such that it can autonomously complete a complex task. The architecture is composed of similar modules, primitive actions, and composition behaviors. These modules, which are derived from a basic template, are capable of learning and cooperating to cope with a variety of tasks. The infrastructure of a template embeds a reinforcement learning mechanism with an adaptable receptive module (ARM)-based critic-actor model. Each template executes one specified behavior and also cooperates with other templates to form a more dexterous composed behavior. In other words, the composed behavior is constructed by several primitives with similar modular architecture. The learning and cooperation abilities in the modules are based on a reinforcement learning technique, which is based on a critic-actor model. The proposed architecture is implemented in a field-programmable gate array (FPGA) chip with a CPU core such that the computing device can fully utilize the merits of parallel processing of neural networks in the ARM scheme. The study is demonstrated on a mobile robot for goal-seeking, cruise , and safety ensurance tasks in an unstructured environment with obstacles such as walls and blocks. The results show that this robot with the modular architecture can perform well in unstructured environments.